NCA-GENM Question 208
Select 3You are part of a team deploying a generative AI model for multimodal applications, including text and image generation. During testing, you discover that the model occasionally generates biased content that reinforces harmful stereotypes. Which principles of trustworthy AI should you prioritize to address this issue?
- A
Fairness to ensure the model does not produce biased outputs that harm specific groups.
- B
Transparency to provide users with clear explanations of how the model makes decisions.
- C
Performance optimization to improve the model's accuracy in generating content.
- D
Accountability to establish clear ownership of responsibility for the model's outputs.
- E
Generative creativity to enhance the diversity of outputs and ensure innovation.
Show answer and explanation
Correct answers: A, B, D
Explanation
The ethical principles of trustworthy AI, such as fairness, transparency, and accountability, are critical in mitigating issues like bias and harmful stereotypes in generative AI models. Fairness ensures equitable treatment, transparency supports understanding and debugging, and accountability establishes responsibility for addressing harmful outcomes. Together, these principles help create AI systems that are more ethical and reliable.
- A. Correct.
Fairness is critical in addressing issues like biased outputs, ensuring the model treats all groups equitably and avoids reinforcing harmful stereotypes.
- B. Correct.
Transparency allows users and stakeholders to understand how the model operates and identify potential sources of bias or errors.
- C. Incorrect.
While performance optimization is important, it does not directly address ethical concerns like bias or accountability, making it less relevant in this specific context.
- D. Correct.
Accountability ensures that there are clear mechanisms for identifying who is responsible for addressing and mitigating harmful outputs, making it a key principle in this scenario.
- E. Incorrect.
Generative creativity focuses on enhancing diversity in outputs but does not directly address ethical concerns like fairness, transparency, or accountability.